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1 edition of The use of chaos metrics to analyze Lagrangian particle diffusion models found in the catalog.

The use of chaos metrics to analyze Lagrangian particle diffusion models

Korey V. Jackson

The use of chaos metrics to analyze Lagrangian particle diffusion models

by Korey V. Jackson

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  • 8 Currently reading

Published by Naval Postgraduate School, Available from the National Technical Information Service in Monterey, Calif, Springfield, Va .
Written in English


Edition Notes

ContributionsKamada, Ray
The Physical Object
Pagination153 p. ;
Number of Pages153
ID Numbers
Open LibraryOL25500961M

Lagrangian description of particle tracking to model two-phase flow and expose development process in modeling such phenomena. 2. Characteristics of Lagrangian description in turbulent flow Empirical probes have shown that three impacts have to be allowed for, to predict particle dispersion precisely. The microscopic details of the fast chaotic dynamics are not relevant for the emergence of stochastic Lagrangian particle dynamics, but they do feature in the diffusion tensor in equation. In order to build reliable stochastic coarse-grained fluid models, one needs to determine the drift and diffusion terms () and (), by:

applies to each particle. For an N particle system in 3 dimensions, there are 3N second order ordinary differential equations in the positions of the particles to solve for.. Instead of forces, Lagrangian mechanics uses the energies in the system. The central quantity of Lagrangian mechanics is the Lagrangian, a function which summarizes the dynamics of the entire system.   I would define 6 different phases in MODELS>Lagrangian Multiphase > Lagr. phases, one for each injector. This way you can see the tracks for each injector separately. To see the tracks, you have to turn ON Track model in Models for each phase.

02 Nov 02 Nov The Lagrangian chemistry and transport model ATLAS: validation of advective transport and mixing. I. Wohltmann and M. RexCited by: A collection of such particle trajectories can be used for analyzing the Lagrangian dynamics of the fluid motion, for performing Lagrangian statistics of various flow quantities etc. In computational fluid dynamics, the Lagrangian particle tracking (or in short LPT method) is a numerical technique for simulated tracking of particle paths.


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The use of chaos metrics to analyze Lagrangian particle diffusion models by Korey V. Jackson Download PDF EPUB FB2

The Use of Chaos Metrics to Analyze Lagrangian Particle Diffusion Models by Korey V. Jackson Major, United States Army B.S., South Dakota State University, M.S., Florida Institute of Technology, Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN PHYSICS from the.

IVE Thisstudyexaminestheself-affinefractaldimension,DA andcomparesittotheShannonentropy,S,andtheLyapunov exponent,etricsarefirstappliedtothel.

The use of chaos metrics to analyze Lagrangian particle diffusion models. By Korey V. Jackson. Download PDF (7 MB) distribution is unlimitedChaos metrics are examined as a tool to analyze atmospheric three-dimensional dispersion models at the individual particle rather than the aggregate level.

These include the self-affine fractal Author: Korey V. Jackson. Cover title "NPS-PH" "Janu " AD A Includes bibliographical references (p.

) The Lorenz and Henon attractors and two atmospheric Lagrangian particle models were tested using self-affine fractal dimension, DA, Shannon information entropy, S, and the Lyapunov exponent, lambda, along with turbulent kinetic energy, vertical variance, and Brunt-Vaisala Pages: THE USE OF CHAOS METRICS TO ANALYZE LAGRANGIAN PARTICLE DIFFUSION MODELS.

By Dudley Knox Library. Abstract. The use of chaos metrics to analyze Lagrangian particle diffusion models Jackson, Korey V Topics: If applicable. Year: OAI identifier: oai: Author: Dudley Knox Library. Chaos metrics for testing lagrangian particle models. Results show that (1) chaos metrics are a new set of tools to assess the micro behavior of Lagrangian particle models.

However power Author: Ray Kamada. A study of Lagrangian chaos, Eulerian chaos, and mixing enhancement in converging–diverging channel flows, using spectral element direct numerical simulations, is presented. The time‐dependent, incompressible Navier–Stokes and continuity equations are solved for laminar, transitional, and chaotic flow regimes for ≤Re≤ Classical fluid dynamics representations and dynamical Cited by: The Lagrangian models are based on a Langevin equation for the velocity of a fluid particle, and can take a number of different forms, depending mainly on the type of turbulence being simulated.

We briefly review these forms, concentrating on the theoretically correct version for simulating dispersion in a convective boundary layer (CBL).Cited by: 3. Another successful resampling approach to rare event sampling is that of population dynamics, also called interacting particle algorithms or genealogical particle analysis.

In such methods, resampling of an ensemble of trajectories is performed at predetermined time points in the simulation, when the most promising realizations are selected and Author: Freddy Bouchet, Joran Rolland, Jeroen Wouters. The offline Lagrangian particle model FLEXPART–NorESM/CAM (v1): model description and comparisons with the online NorESM transport scheme and with the reference FLEXPART model.

LAGRANGIAN PARTICLE DISPERSION MODEL. The Lagrangian particle dispersion model presented by Thomson was used in this paper. This model is superior to conventional Lagrangian particle models in its capability of rational application to inhomogeneous and non-Gaussian turbulent fields, and of using Gaussian random by: 1.

Lagrangian or particle tracking models A release of radionuclides is simulated by a number of discrete particles. Each particle is equivalent to a number of units. The paths followed by each particle is computed individually. Diffusion and decay are calculated by a Monte Carlo method.

At the end of the simulation, the density of particles per waterFile Size: KB. Lagrangian analysis through virtual particle tracking within OGCMs began in the s, on small-scale structures, with studies on a theoretical box-model (Awaji et al., ) as well as a model that incorporated hydrographic data and realistic topography (Imasato et al., ).The Lagrangian framework of these small-scale examples was then applied to the velocity-field output of basin-scale Cited by:   In addition to the Lagrangian integral time T L, it is common to use the Lagrangian integral scale L L, and it is often assumed (for example, Lumpkin et al.

11) that as the Lagrangian particle Cited by: Lagrangian particle models compute trajectories of a large number of so-called particles to describe the transport and diffusion of tracers in the atmosphere. The main advantage of Lagrangian models is that, unlike in Eulerian models, there is no numerical diffusion.

The HYSPLIT Hybrid Single-Particle Lagrangian Integrated Trajectory Model has. This work focuses on the numerical analysis of one‐dimensional nonlinear diffusion equations involving a convolution product. First, homogeneous friction equations are considered.

Algorithms follow recent ideas on mass transportation methods and lead to simple schemes which can be proved to be stable, to decrease entropy, and to converge toward the unique solution of the continuous by: generated from one temperature particle. This kind of idea was based on the fact that the slope of a temperature element is precisely approximated by the density distribution of two Gaussian particles with opposite strength.

In addition to these two models, the diffusion velocity method (Ogami and Akamatsu, ) was used in order to handle. EVENT DETECTION IN CROWDS OF PEOPLE BY INTEGRATING CHAOS AND LAGRANGIAN PARTICLE DYNAMICS C. SPAMPINATO, A. FARO, S. PALAZZO Lagrangian Particle Dynamics Theory and, 3) the last one uses self organizing maps (SOM) for segmenting the flow motion map in order to detect events.

tracking, flow models, spatio-temporal analysis of shapes and. A temporal complex network-based approach is proposed as a novel formulation to investigate turbulent mixing from a Lagrangian viewpoint. By exploiting a spatial proximity criterion, the dynamics of a set of fluid particles is geometrized into a time-varying weighted by: 4.

strength of Lagrangian models is their ability to explicitly calculate near and far field diffusion. Another strength of Lagrangian models is their capability to simul- ate counter-gradient transport (Raupach, ; Wilson, ).

An extensive body of theoretical work exists on Lagrangian modelling of turbulent diffusion. S. Unterstrasser and I. Sölch: NSIP-optimisation in a Lagrangian ice microphysical model The present study is based on the Lagrangian particle tracking model EULAG-LCM (Sölch and Kärcher, ).

In Lagrangian models the amount of CPU time (and also mem-ory and storage data) depends heavily on the number of sim-Cited by: Nevertheless, particle methods provide outstanding advantages over other air pollution diffusion modeling techniques, such as Gaussian models and grid models, as discussed below.

Keywords Inversion Layer Particle Method Plume Rise Potential Cited by: 9.Early numerical models attempted to couple Lagrangian particle methods with tra-ditional Eulerian uid turbulence simulations (Riley & Patterson ; Elgobashi & Truesdell ; Squires & Eaton a,b).

In the most simple limit, trajectory of the particles can be determined by use of the Stokes drag formula (Stokes ). In this.